Supervisory Control And Data Acquisition Research Articles

DESIGN OF A SCADA SYSTEM FOR A SOLAR PHOTOVOLTAIC POWER PLANT

This paper presents the design and implementation of a solar panel data monitoring system using a SCADA (Supervisory Control and Data Acquisition) system. The system is built via the Siemens S7-1200 Programmable Logic Controller (PLC) and programmed using TIA (Totally Integrated Automation) Portal V17 software. The system includes various sensors and instrumentation for monitoring solar panel performance, such as temperature sensors, current and voltage sensors, and irradiance sensors. The PLC collects data from these sensors and transmits it to the SCADA software, which displays real-time data on the graphical user interface (GUI). The system also includes features such as alarms and notifications for abnormal conditions, data logging for historical analysis, and remote access for monitoring and control from a central location. The system's reliability and efficiency are enhanced through the use of the S7-1200 PLC and TIA Portal V17 software, which provide advanced programming and automation capabilities. Therefore, this solar panel data monitoring system provides a comprehensive solution for monitoring and optimizing the performance of solar panel systems, helping to increase efficiency, reduce downtime, and improve overall system performance.

Development of an IoT-Based SCADA System for Monitoring of Plant Leaf Temperature and Air and Soil Parameters

Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 × 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters.

Introducing a New Method for DPMU in Detecting the Type and Location of the Fault

Introduction: Nowadays, due to the increasing development of distribution networks, their safety and high-reliability performance are of particular importance. One of the most important problems that endanger the security and reliability of these networks is the creation of some faults in them. In case of a fault in the network, identifying its location and type can be of great help in repairing faulty equipment. Also, by detecting the disconnection of one of the equipment or lines, it is possible to prevent accidents in the network. Methods: Phasor Measurement Unit (PMU) has been widely and successfully used as Transmission- Phasor Measurement Unit (T-PMU). The reporting time of PMUs is much shorter than the old Supervisory Control and Data Acquisition (SCADA) systems. They can provide synchronized phasor measurements that can generate voltage phasors of different network nodes. This study aimed to investigate the various applications of phasor measurement units in distribution networks and present a new method for detecting and analyzing the location and type of fault and topology fault of the distribution network using the Internet of Things (IOT) analysis method. Results: To implement this method, it is necessary to measure different parameters of the distribution network before and after the occurrence of a fault, which is used by the DPMU for measurement. The simulation results indicate that for both single-topology and multi-topology faults, the proposed method has higher accuracy and better detection than the remaining common methods and effectively detects single-topology and multi-topology faults in the distribution network. Conclusion: This method can provide a more accurate network topology to estimate the state of the distribution network, which improves the accuracy of the state estimation and is suitable for implementing various advanced functions of the distribution management system.

An automated voice command classification model based on an attention-deep convolutional neural network for industrial automation system

In this research, a method was developed for utilizing voice commands with programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems, which are commonly utilized in industrial automation. This approach incorporates artificial intelligence to enable human–machine interaction, aligning with the trends of Industry 4.0. A deep neural network was specifically designed for speech recognition, eliminating the need for reliance on any pre-existing speech-to-text engines. The objective was to create a model that is accurate and compact in size, making it suitable for embedded systems within industrial systems. To train the deep learning network, 21,600 sound files were generated. These files combined real factory noise with a synthetic dataset of human speech, forming a dataset comprising 60 different classes of voice commands. These commands encompassed actions like starting, stopping, and operating at various speeds for 10 motors controlled by the automation system. After applying the Mel-frequency cepstral coefficient (MFCC) to the voice commands, the resulting data was directly fed into the proposed network. The network achieved an impressive accuracy rate of 99.73%. Notably, the proposed network outperformed even networks several times its size.

Typical fault prediction method for wind turbines based on an improved stacked autoencoder network

Abstract Timely prediction of wind turbine states is valuable for reduction of potential significant losses resulting from deterioration of health condition. To enhance the accuracy of fault diagnosis and early warning, data collected from supervisory control and data acquisition (SCADA) system of wind turbines is graphically processed and used as input for a deep learning mode, which effectively reflects the correlation between the faults of different components of wind turbines and the multi-state information in SCADA data. An improved stacked autoencoder (ISAE) framework is proposed to address the issue of ineffective fault identification due to the scarcity of labeled samples for certain fault types. In the data augmentation module, synthetic samples are generated using SAE to enhance the training data. Another SAE model is trained using the augmented dataset in the data prediction module for future trend prediction. The attribute correlation information is embedded to compensate for the shortcomings of SAE in learning attribute relationships, and the optimal factor parameters are searched using the particle swarm optimization (PSO) algorithm. Finally, the state of wind turbines is predicted using a CNN-based fault diagnosis module. Experimental results demonstrate that the proposed method can effectively predict faults and identify fault types in advance, which is helpful for wind farms to take proactive measures and schedule maintenance plans to avoid significant losses.

Outage Analysis of Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer System for Industrial Emergency Applications.

In the scenario of a natural or human-induced disaster, traditional communication infrastructure is often disrupted or even completely unavailable, making the employment of emergency wireless networks highly important. In this paper, we consider an industrial Supervisory Control and Data Acquisition (SCADA) system assisted by an unmanned aerial vehicle (UAV) that restores connectivity from the master terminal unit (MTU) to the remote terminal unit (RTU). The UAV also provides power supply to the ground RTU, which transmits the signal to the end-user terminal (UT) using the harvested RF energy. The MTU-UAV and UAV-RTU channels are modeled through Nakagami-m fading, while the channel between the RTU and the UT is subject to Fisher-Snedecor composite fading. According to the channels' characterization, the expression for evaluating the overall probability of outage events is derived. The impact of the UAV's relative position to other terminals and the amount of harvested energy on the outage performance is investigated. In addition, the results obtained based on an independent simulation method are also provided to confirm the validity of the derived analytical results. The provided analysis shows that the position of the UAV that leads to the optimal outage system performance is highly dependent on the MTU's output power.

Data-driven Pump Performance Analysis Using Online Monitoring Data Accumulated with Supervisory Control and Data Acquisition System

Pump degradation has significant impacts on the operation of water treatment plants since pumps are widely used as basic facilities to supply and distribute water. The authors develop a method for quantifying pump performance to monitor pump degradation by using online monitoring data accumulated with a supervisory control and data acquisition (SCADA) system. The developed method estimates the performance of each pump from the measured values at the confluence of pipes with the least-squares method and respective on-off signals. From the viewpoint of practicality, the usefulness of the developed method was evaluated.

Heuristic Evaluation of Supervisory Control and Data Acquisition (SCADA) Displays

Supervisory Control and Data Acquisition (SCADA) is a computer software architecture that collects, analyzes, and delivers real-time production data to an accompanying human-machine interface (HMI) display. SCADA displays can help ensure efficiency in industrial settings by allowing users to monitor and control production processes, see system issues, and directly interact with equipment or devices (e.g., sensors, motors). However, SCADA displays tend to be highly complex and cluttered with data and interactive components. In this research, we conducted an evaluation of various SCADA displays available online using Neilson’s ten usability heuristics. The findings revealed that those displays scored poorly in the evaluation, indicating the need for redesign concepts that prioritize user needs. We then provide redesigned displays, conduct computational modeling to ensure the effectiveness of the redesigns, and propose a set of SCADA display design guidelines aimed at improving those displays to support user needs and, ultimately, improve user performance.

Spatio-Temporal Generative Adversarial Network Based Power Distribution Network State Estimation With Multiple Time-Scale Measurements

The increasing penetration of distributed renewable generation has introduced significant uncertainties and randomness to the power distribution network operation. Accurate and timely awareness of the network operation is of paramount importance to ensure system safety and reliability and is considered non-trivial and costly as substantial network reinforcement with advanced measurement devices is generally required. Also, the existing state estimation methods, e.g., weighted least square (WLS), may not converge in the presence of incomplete and inaccurate measurements. This paper proposes a spatio-temporal estimation generative adversarial network (ST-EGAN) consisting of feature extraction, information completion, data reconstruction and fake data discrimination to generate high-resolution pseudo-measurements to promote the accuracy and robustness of state estimation. The task of high-resolution power distribution network state estimation is carried out based on the mixed dataset of multiple time-scale measurements obtained from Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU). The proposed solution is extensively assessed using the IEEE 33-bus test network compared with the existing solutions for a range of scenarios with different resolutions and noise intensities. The numerical results demonstrated that the proposed ST-EGAN can reduce the mean RMSE by 4.78% compared to interpolation algorithms, and reduce the RMSE by 0.14% and 0.21% compared with deep convolutional generative adversarial networks (DCGAN) and super-resolution convolutional networks (SRCNN) in the presence of noises with different intensities. The proposed method can be generalized to cases with different topological structures and measurement assembly conditions.

Unknown Attack Traffic Classification in SCADA Network Using Heuristic Clustering Technique

Attack Traffic Classification (ATC) technique is an essential tool for Industrial Control System (ICS) network security, which can be widely used in active defense, situational awareness, attack source traceback and so on. At present, the state-of-the-art ATC methods are usually based on traffic statistical features and machine learning techniques, including supervised classification methods and unsupervised clustering methods. However, it is difficult for these methods to overcome the problems of lack of attack samples and high real-time requirement in ATC in Supervisory Control and Data Acquisition (SCADA) networks. In order to address the above problems, we propose a self-growing ATC model based on a new density-based heuristic clustering method, which can continuously and automatically detect and distinguish different kinds of unknown attack traffic generated by various attack tools against SCADA networks in real time. An effective representation method of SCADA network traffic is proposed to further improve the performance of ATC. In addition, a large number of experiments are conducted on a compound dataset consisting of the SCADA network dataset, the attack tool dataset and the ICS honeypot dataset, to evaluate the proposed method. The experimental results show that the proposed method outperforms existing state-of-the-art ATC methods in the crucial situation of only normal SCADA network traffic.

High-Precision Single-Leak Detection and Localization in Single-Phase Liquid Pipelines Using the Negative Pressure Wave Technique: An Application in a Real-Field Case Study

Summary This paper presents a novel workflow for high-precision leak detection in pipeline networks using the negative pressure wave (NPW) technique. The proposed workflow addresses challenges associated with noisy and convoluted pressure transducer data, rapid pressure decay, and the need for robustness in leak event detection. To overcome these challenges, the workflow incorporates data preprocessing techniques for cleansing, normalization, and denoising, as well as dynamic pressure control limit lines to differentiate between pump and leak events. Multiple transducer analysis techniques are used to minimize false positives. Synthetic leak scenarios are generated using the Water Network Tool for Resilience (WNTR) package, enabling a comprehensive assessment of the workflow’s performance. The generated scenarios are validated through pressure history matching against field inline pressure recordings. A dashboard is developed for real-time visualization and verification of leak events. The effectiveness of the workflow is demonstrated through testing on a real network, resulting in the successful detection and precise localization of a confirmed leak event. The workflow proves its capability to achieve high accuracy, with a 100-m resolution in a complex network configuration with 29 pipe sections and 1-Hz pressure signal recordings. For synthetic leak events, a 10-Hz pressure signal is utilized, achieving a remarkable 10-m accuracy. Moreover, the integration of the workflow with supervisory control and data acquisition (SCADA) systems is showcased, highlighting its potential for near real-time leak detection in practical applications. Overall, this paper presents a comprehensive and effective workflow for high-precision leak detection and localization in pipeline networks, offering valuable insights into improving the efficiency and reliability of leak detection systems.

Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.

An Integrated Testbed for Power System Cyber-Physical Operations Training

The increased adoption of information and communication technology for smart grid applications will require innovative cyber–physical system (CPS) testbeds to support research and education in the field. Groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed using a real-time simulator, protection and automation devices, and a supervisory control and data acquisition (SCADA) system. The testbed replicated the performance of smart grids, and the main potential cyber threats that electric grids may face. Different case scenarios include a distribution system protection study, a denial of service (DoS) attack, a jamming attack, a network packet manipulation attack, a sensor data manipulation attack, a false trip command attack, etc. The system’s performance before and after the cyberattacks are studied using packet-sniffing tools and a network packet analyzer. The impact on the grid is analyzed using metrics such as voltage oscillation, frequency deviation, and loss of active power generation. Moreover, the complex interdependencies between the cyber and physical domains are discussed in detail, providing insightful guidelines for key features and design decisions for future smart grid testbeds.

Fatigue Life Evaluation of Wind Turbine Tower Based on Measured Data

Fatigue life is a crucial design factor which dictates the safe operation of the wind turbines, but it is influenced by uncertain factors such as environmental loads, analytical models, material properties, and manufacturing methods. In this study, a 1.5 MW wind turbine was monitored in operation to understand the fatigue mechanism and enhance wind turbine design. The influence of different operating conditions on fatigue damage was analyzed by correlating strain monitoring data with supervisory control and data acquisition (SCADA) data. Furthermore, a fatigue evaluation method based on measured strain data was proposed. Fatigue damage increases with the increase of wind and rotation speed. More than 50% of the damage occurred at the rated rotation speed state, the corresponding wind speed was greater than the rated wind speed and the pitch control system was active. The findings of this study provide insights for investigating the real fatigue state of similar wind turbine towers and improving the return on investment by closely estimating their service life.

Smart IoT-based water treatment with a Supervisory Control and Data Acquisition (SCADA) system process

Abstract Water treatment is necessary to ensure the availability of clean and safe water for various uses. Integrating Internet of Things (IoT) technology with water purification systems has shown enormous potential in recent years for enhancing the efficiency and efficacy of the treatment process. Monitoring the disposal of sewage in treatment facilities is the primary obstacle. As a result, a Supervisory Control And Data Acquisition (SCADA) system, including the IoT, has been proposed to ensure the proper operation of these sewer systems and limit the risk of overflow and malfunction. In this paper, we suggest a novel approach that blends Deep Belief Networks (DBNs) with an IoT-based water treatment system equipped with a SCADA system for increased monitoring and control. An IoT–SCADA system can be implemented at various wastewater collection and treatment phases. Secondly, incorporating DBNs enhances the system's predictive capabilities, enabling proactive maintenance and decision-making to prevent potential failures and optimize resource allocation. The proposed technique computes the efficacy of the effluent treatment facility and ensures that chemical emissions do not exceed permissible limits. Furthermore, Complex Event Processing (CEP) can be utilized to evaluate and analyze the massive influx of real-time data sets provided by IoT sensors.

A new low‐cost and low‐power industrial internet of things infrastructure for effective integration of distributed and isolated systems with smart grids

AbstractInternet of Things (IoT) has recently become one of the fast‐growing technologies in the world. Integrating existing systems with smart grids still represents a significant challenge arising from the massive cost of upgrading the infrastructure. This paper provides an IoT infrastructure solution based on a newly designed low‐cost microcontroller‐based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. The IoT RTU supports Wi‐Fi communication and includes digital/analog inputs and outputs besides the advantage of serial data transmission standard RS‐485 as an industrial field communication (IFC). The Modbus communication protocol is utilized as a serial industrial protocol that can be interfaced over the RS‐485 standard. The advantage of having an IFC protocol integrated into the IoT infrastructure enables to link any supported device and publish the required data to the cloud, besides the ability to add smart metering technologies, which allows Advanced Metering Infrastructure (AMI) in these areas. IoT‐enabled Supervisory Control and Data Acquisition (SCADA) system is integrated with the proposed IoT infrastructure for its advantages of remote monitoring and control. The proposed IoT infrastructure is characterized by proper data security and low power consumption. The IoT infrastructure is simulated with an existing real application for proof of concept, and the results are discussed for different case studies.

Performance analysis of artificial lift systems deployed in natural gas wells: A time-series analytics approach

In Coal Seam Gas (CSG) production, Artificial Lift (AL) systems comprising various downhole pumps produce coal-fine-laden water. Over time, the accumulation of coal fines results in mechanical failures of such pumps, which leads to a loss in natural gas production. This problem is exacerbated by CSG operators having to manage thousands of wells in their day-to-day operations. The reason for having such an exuberant number of natural gas wells is due to the fact that CSG reservoirs have a short production cycle when compared to conventional reservoirs. Therefore, thousands of CSG wells must operate simultaneously to sustain natural gas volumes that satisfy domestic energy demand, and help meet contractual export obligations.To manage a large fleet of CSG wells, operators rely on Petroleum and Surveillance Engineers to mitigate production issues and advise timely corrective actions. Current methods of monitoring CSG wells involve observing real-time trends and alarms from Supervisory Control and Data Acquisition (SCADA) systems. However, managing a large fleet of wells with a monitoring-only approach can be detrimental to AL operations. Methods such as Exception Based Surveillance have been used to improve engineers' workload, but such methods are informative at best. In addition, they do not provide early indications of changed downhole pump performance. To improve how AL systems are monitored in CSG operation, we propose a novel well surveillance method that can help mitigate pump failure and provide engineers with insightful information to take corrective actions.In this work, we will present an innovative time-series analytics approach that examines the performance of AL systems in near real-time and helps Well Surveillance and Production Engineers make timely decisions that aid in mitigating pump failures. We used time-series data from 448 CSG wells to build the streaming analytics methodology that autonomously detects various performance states of downhole pumps during CSG production. Furthermore, we successfully detected the onset of AL systems failures, such as solids build-up, gas intake, high torque and pump degradation. We validated our solution on a separate set of 428 wells and were able to show reliable detection of detrimental events on live data from producing natural gas wells. This solution is currently deployed by 2 CSG operators where real-time notifications aid Petroleum and Well Surveillance Engineers with proactively managing AL systems across multiple CSG assets.

Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques

Wakes between neighboring wind turbines are a significant source of energy loss in wind farm operations. Extensive research has been conducted to analyze and understand wind turbine wakes, ranging from aerodynamic descriptions to advanced control strategies. However, there is a relatively overlooked research area focused on characterizing real-world wind farm operations under wake conditions using Supervisory Control And Data Acquisition (SCADA) parameters. This study aims to address this gap by presenting a detailed discussion based on SCADA data analysis from a real-world test case. The analysis focuses on two selected wind turbines within an onshore wind farm operating under wake conditions. Operation curves and data-driven methods are utilized to describe the turbines’ performance. Particularly, the analysis of the operation curves reveals that a wind turbine operating within a wake experiences reduced power production not only due to the velocity deficit but also due to increased turbulence intensity caused by the wake. This effect is particularly prominent during partial load operation when the rotational speed saturates. The turbulence intensity, manifested in the variability of rotational speed and blade pitch, emerges as the crucial factor determining the extent of wake-induced power loss. The findings indicate that turbulence intensity is strongly correlated with the proximity of the wind direction to the center of the wake sector. However, it is important to consider that these two factors may convey slightly different information, possibly influenced by terrain effects. Therefore, both turbulence intensity and wind direction should be taken into account to accurately describe the behavior of wind turbines operating within wakes.

Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis

Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on I2d, I2e, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems.

A security model for smart grid SCADA systems using stochastic neural network

AbstractDetection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.